A Note on Decomposition of Sources of Variability in Perceptual Decision-making.

Information processing underlying human perceptual decision-making is inherently noisy and identifying sources of this noise is important to understand processing. Ratcliff, Voskuilen, and McKoon (2018) examined results from five experiments using a double-pass procedure in which stimuli were repeated typically a hundred trials later. Greater than chance agreement between repeated tests provided evidence for trial-to-trial variability from external sources of noise.

Modeling 2-alternative forced-choice tasks: Accounting for both magnitude and difference effects.

We present a model-based analysis of two-alternative forced-choice tasks in which two stimuli are presented side by side and subjects must make a comparative judgment (e.g., which stimulus is brighter).

Internal and external sources of variability in perceptual decision-making.

It is important to identify sources of variability in processing to understand decision-making in perception and cognition. There is a distinction between internal and external variability in processing, and double-pass experiments have been used to estimate their relative contributions. In these and our experiments, exact perceptual stimuli are repeated later in testing, and agreement on the 2 trials is examined to see if it is greater than chance.

Aging and confidence judgments in item recognition.

We examined the effects of aging on performance in an item-recognition experiment with confidence judgments. A model for confidence judgments and response time (RTs; Ratcliff & Starns, 2013) was used to fit a large amount of data from a new sample of older adults and a previously reported sample of younger adults. This model of confidence judgments allows us to distinguish between changes evidence from memory and changes in decision-related components and it accounts for both RT distributions and response proportions.

Comparing fixed and collapsing boundary versions of the diffusion model.

Optimality studies and studies of decision-making in monkeys have been used to support a model in which the decision boundaries used to evaluate evidence collapse over time. This article investigates whether a diffusion model with collapsing boundaries provides a better account of human data than a model with fixed boundaries. We compared the models using data from four new numerosity discrimination experiments and two previously published motion discrimination experiments.

Modeling confidence and response time in associative recognition.

Research examining models of memory has focused on differences in the shapes of ROC curves across tasks and has used these differences to argue for and against the existence of multiple memory processes. ROC functions are usually obtained from confidence judgments, but the reaction times associated with these judgments are rarely considered. The RTCON2 diffusion model for confidence judgments has previously been applied to data from an item recognition paradigm.